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A practical guide to evaluating build, buy, or hybrid RAG strategies for secure, scalable, and production-ready enterprise AI deployments.
Enterprise AI teams are under growing pressure to move Retrieval-Augmented Generation, or RAG, from promising prototypes to trusted production systems. A proof of concept may work with clean files and expected questions, but production exposes a harder reality: ingestion, retrieval quality, permissions, source attribution, evaluation, observability, cost control, and long-term maintenance all become operational requirements.
McKinsey’s latest survey found that 88% of surveyed organizations report regular use of artificial intelligence in at least one business function, but only about one-third have begun scaling their programs. 1
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